arXiv:2608. 01217v1 Announce Type: cross Abstract: Local-stochastic volatility (LSV) combines vanilla marginals with richer smile dynamics, but calibration requires a slow, noisy and sequential McKean--Vlasov fixed point.
By Xiaozhen Wang, Ana\"is Despr\'es, Martin Dureau, Francois Buet-Golfouse
Werracle is a zero‑storage on‑chain AI decision oracle that fits into a single 32‑byte EVM storage slot and uses procedural Mandelbrot dynamics to generate continuous non‑linear decision hyperplanes from a 24‑byte coordinate triplet. Implemented in pure Solidity bytecode with fixed‑point arithmetic, it evaluates a 16‑point Pareto micro‑grid in only 21,438 gas, achieving sub‑millisecond latency on Layer‑2 rollups. The protocol is formally verified with a 1,000‑test deterministic suite and is deployed live on an EVM devnet, demonstrated through a Uniswap v4 dynamic fee governor that adjusts liquidity provider fees in real time.
By Volkan Da\u{g}l{\i}, Zerrin Da\u{g}l{\i}, Da\u{g}han Da\u{g}l{\i}
The paper introduces asymptotically-informed neural‑network architectures for computing Black‑Scholes implied volatility. By learning a trainable partition of the price‑log‑moneyness domain and combining specialised local approximations, the models outperform standard feed‑forward networks across a wide range of parameters. The neural‑network outputs also serve as highly accurate initial guesses for a third‑order Householder scheme, enabling near machine‑precision results after only two refinement iterations.
By Samira Amiriyan, Youness Boutaib
arXiv:2606. 01122v1 Announce Type: new Abstract: We propose a five-step diagnostic protocol for residual-trained neural HJB-PIDE solvers with control-dependent L\'evy jumps, targeting a general failure mode of neural PDE methods: a learned solution can match headline scalar diagnostics while miscomputing an operator inside its training loss.
By R. Drissi
arXiv:2606. 16961v1 Announce Type: new Abstract: We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule.
By Sadanand Singh, Allam Reddy, Manan Chopra
arXiv:2607. 20171v1 Announce Type: cross Abstract: Learned solvers for compressible flow are usually compared to classical methods at equal mesh resolution rather than at equal computational cost, and they typically offer no guarantee that their solutions remain physically admissible.
By Denis Gueyffier (ONERA -- Institut Polytechnique de Paris)